Continuous Control
427 papers with code • 73 benchmarks • 10 datasets
Continuous control in the context of playing games, especially within artificial intelligence (AI) and machine learning (ML), refers to the ability to make a series of smooth, ongoing adjustments or actions to control a game or a simulation. This is in contrast to discrete control, where the actions are limited to a set of specific, distinct choices. Continuous control is crucial in environments where precision, timing, and the magnitude of actions matter, such as driving a car in a racing game, controlling a character in a simulation, or managing the flight of an aircraft in a flight simulator.
Libraries
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Most implemented papers
Parameter Space Noise for Exploration
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
Off-Policy Deep Reinforcement Learning without Exploration
Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for data collection.
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
The overestimation bias is one of the major impediments to accurate off-policy learning.
Learning Latent Dynamics for Planning from Pixels
Planning has been very successful for control tasks with known environment dynamics.
Continuous Deep Q-Learning with Model-based Acceleration
In this paper, we explore algorithms and representations to reduce the sample complexity of deep reinforcement learning for continuous control tasks.
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature.
DeepMind Control Suite
The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents.
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations.
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control.
Sample Efficient Actor-Critic with Experience Replay
This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems.